Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

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If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

You've reached the end of this course. I hope you've learned something while
reading it, I sure learned a lot writing it. Trying to explain something is a
generally a good exercise to test for your knowledge of this thing. Of course,
we only scratched the surface of NumPy and there are many things left to
discover.

If there's a single message to retain from this course it is "premature
optimization is the root of all evil". We've seen that code vectorization can
drastically improve your computation, with several orders of magnitude in some
cases. Still, problem vectorization is generally much more powerful. If you
write code vectorization too early in your design process, you won't be able to
think out-of-the-box and you'll certainly miss some really powerful alternatives
because you won't be able to identify your problem properly as we've seen in the problem vectorization chapter. This requires some experience and
you have to be patient: experience is not an overnight process.

Finally, custom vectorization is an option worth considering once you've looked
at the alternatives to NumPy. When nothing works for you, NumPy still offers
you a clever framework to forge your own tools. And who knows, this can be the
start of an exciting adventure for you and the community as it happened to me
with the [glumpy](http://glumpy.github.io) and the [vispy](http://vispy.org) packages.

This lesson sums up the discussion on Python to NumPy.

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

A Quick Overview